Introduction

In the last three years there has been a seismic shift in what investors value. No longer are they looking to corporations for merely a return on investment, but a return on social capital. Social Capital has been defined in many ways by many people, but I define social capital in terms how corporations treat the Environment, Society, and how they Govern their employees.Taking the first initial from the words: Environment, Society, and Governance, yields the acronym ESG. ESG has become the de-facto measure investors use to grade corporations’ social capital.

In terms of the E in ESG, investors are looking to invest in renewable projects. Once such project type is Renewable Natural Gas. Renewable Natural Gas is natural gas that comes from non-fossil sources, such as landfills and farms. Landfills can products methane, the key component in natural gas, that is released by means of decaying bio-material found in trash. Farm animals also release methane through bodily waste that can be captured in an anaerobic digestion system.

My report seeks to answer four questions for investors:

Required Packages

The following libraries were essential in order to complete my project:

Source Data

The source of my data originated from the Environmental Protection Agency, better known by it’s acronym, the EPA. The EPA provides a wealth of data on renewable natural gas projects in both the Landfill and the farm animal flavor. The farm animal projects are better known as AgStar projects.

The landfill data will be loaded from the LFG Energy Project Data Files as of March 2022. The location of this file can be located from the following URL: Source: https://www.epa.gov/lmop/lmop-landfill-and-project-database

#Loading in landfill project data
landfillgas_raw = read_excel("Data/lmopdata.xlsx", sheet="LMOP Database")

The AgSTAR Livestock Anaerobic Digester Database can be loaded from the following URL: https://www.epa.gov/agstar/livestock-anaerobic-digester-database

AgSTAR publishes updated data on this website periodically (generally, two times per year) to enhance public access to information and support the development of biogas recovery projects

#Loading AgStar project data
anaerobicdigesterfas_raw = read_excel("Data/agstar-livestock-ad-database.xlsx", sheet="Operational and Construction")

The Renewable Natural Gas, or RNG, facility data contains details of 173 facilities around the United States.

#Loading RNG Facility data
rng_facility_raw = read_csv("Data/RNG_Facilities.csv", show_col_types = FALSE)

Data Summary

The data describes the physical and geo location of landfill in the United States. The data also contains project information such as when the project started, the amount of wastes in tons the landfill holds, the amount of gas and, or amount of electricity that the landfill produces as well as emission reduction and avoidance data. The Landfill data consists of the following schema:

#Show names of the columns
names(landfillgas_raw)
##  [1] "GHGRP ID"                                               
##  [2] "Landfill ID"                                            
##  [3] "Landfill Name"                                          
##  [4] "State"                                                  
##  [5] "Physical Address"                                       
##  [6] "City"                                                   
##  [7] "County"                                                 
##  [8] "Zip Code"                                               
##  [9] "Latitude"                                               
## [10] "Longitude"                                              
## [11] "Ownership Type"                                         
## [12] "Landfill Owner Organization(s)"                         
## [13] "Year Landfill Opened"                                   
## [14] "Landfill Closure Year"                                  
## [15] "Current Landfill Status"                                
## [16] "Waste in Place (tons)"                                  
## [17] "Waste in Place Year"                                    
## [18] "LFG Collection System In Place?"                        
## [19] "LFG Collected (mmscfd)"                                 
## [20] "LFG Flared (mmscfd)"                                    
## [21] "Project ID"                                             
## [22] "Current Project Status"                                 
## [23] "Project Name"                                           
## [24] "Project Start Date"                                     
## [25] "Project Shutdown Date"                                  
## [26] "Project Type Category"                                  
## [27] "LFG Energy Project Type"                                
## [28] "RNG Delivery Method"                                    
## [29] "Actual MW Generation"                                   
## [30] "Rated MW Capacity"                                      
## [31] "LFG Flow to Project (mmscfd)"                           
## [32] "Current Year Emission Reductions (MMTCO2e/yr) - Direct" 
## [33] "Current Year Emission Reductions (MMTCO2e/yr) - Avoided"

The data describes the physical location of Anaerobic digesters in the United States. The data also contains project information such as when the farm become operational, the type of animal/farm, the amount of gas and, or amount of electricity that the farm produces as well as emission reduction data. The AgSTAR Anaerobic Digester data consists of the following schema:

#Show names of the columns
names(anaerobicdigesterfas_raw)
##  [1] "Project Name"                                  
##  [2] "Project Type"                                  
##  [3] "City"                                          
##  [4] "County"                                        
##  [5] "State"                                         
##  [6] "Digester Type"                                 
##  [7] "Status"                                        
##  [8] "Year Operational"                              
##  [9] "Animal/Farm Type(s)"                           
## [10] "Cattle"                                        
## [11] "Dairy"                                         
## [12] "Poultry"                                       
## [13] "Swine"                                         
## [14] "Co-Digestion"                                  
## [15] "Biogas Generation Estimate (cu_ft/day)"        
## [16] "Electricity Generated (kWh/yr)"                
## [17] "Biogas End Use(s)"                             
## [18] "System Designer(s)_Developer(s) and Affiliates"
## [19] "Receiving Utility"                             
## [20] "Total Emission Reductions (MTCO2e/yr)"         
## [21] "Awarded USDA Funding?"

The data describes the physical and geo-location of RNG facilities in the United States. The data also contains the amount of gas the facility produces.

The RNG facility data consists of the following schema:

#Show names of the columns
names(rng_facility_raw)
##  [1] "Facility Name"              "Category"                  
##  [3] "Type"                       "City"                      
##  [5] "State"                      "Start date"                
##  [7] "Gas Flow to project (scfm)" "Source"                    
##  [9] "Date updated"               "Latitude"                  
## [11] "Longitude"                  "x"                         
## [13] "y"

Data Cleaning

As you have seen from the previous schema, many of the column names consist of spaces. We now use the janitor library to transform the columns names so that any special character will be replaces by an underscore.

The following removes special characters from the Landfill columns and replaces them with underscores.

#Clean landfill data columns
landfillgas_clean = clean_names(landfillgas_raw)
names(landfillgas_clean)
##  [1] "ghgrp_id"                                           
##  [2] "landfill_id"                                        
##  [3] "landfill_name"                                      
##  [4] "state"                                              
##  [5] "physical_address"                                   
##  [6] "city"                                               
##  [7] "county"                                             
##  [8] "zip_code"                                           
##  [9] "latitude"                                           
## [10] "longitude"                                          
## [11] "ownership_type"                                     
## [12] "landfill_owner_organization_s"                      
## [13] "year_landfill_opened"                               
## [14] "landfill_closure_year"                              
## [15] "current_landfill_status"                            
## [16] "waste_in_place_tons"                                
## [17] "waste_in_place_year"                                
## [18] "lfg_collection_system_in_place"                     
## [19] "lfg_collected_mmscfd"                               
## [20] "lfg_flared_mmscfd"                                  
## [21] "project_id"                                         
## [22] "current_project_status"                             
## [23] "project_name"                                       
## [24] "project_start_date"                                 
## [25] "project_shutdown_date"                              
## [26] "project_type_category"                              
## [27] "lfg_energy_project_type"                            
## [28] "rng_delivery_method"                                
## [29] "actual_mw_generation"                               
## [30] "rated_mw_capacity"                                  
## [31] "lfg_flow_to_project_mmscfd"                         
## [32] "current_year_emission_reductions_mmtco2e_yr_direct" 
## [33] "current_year_emission_reductions_mmtco2e_yr_avoided"

The following removes special characters from the AgSTAR Anaerobic Digester Data columns and replaces them with underscores.

#Clean AgStart Anaerobic Digester data columns
anaerobicdigesterfas_clean = clean_names(anaerobicdigesterfas_raw)
names(anaerobicdigesterfas_clean)
##  [1] "project_name"                                
##  [2] "project_type"                                
##  [3] "city"                                        
##  [4] "county"                                      
##  [5] "state"                                       
##  [6] "digester_type"                               
##  [7] "status"                                      
##  [8] "year_operational"                            
##  [9] "animal_farm_type_s"                          
## [10] "cattle"                                      
## [11] "dairy"                                       
## [12] "poultry"                                     
## [13] "swine"                                       
## [14] "co_digestion"                                
## [15] "biogas_generation_estimate_cu_ft_day"        
## [16] "electricity_generated_k_wh_yr"               
## [17] "biogas_end_use_s"                            
## [18] "system_designer_s_developer_s_and_affiliates"
## [19] "receiving_utility"                           
## [20] "total_emission_reductions_mtco2e_yr"         
## [21] "awarded_usda_funding"

The following removes special characters from the RNG facility data columns and replaces them with underscores.

#Clean RNG Facility data columns
rng_facility_clean = clean_names(rng_facility_raw)
names(rng_facility_clean)
##  [1] "facility_name"            "category"                
##  [3] "type"                     "city"                    
##  [5] "state"                    "start_date"              
##  [7] "gas_flow_to_project_scfm" "source"                  
##  [9] "date_updated"             "latitude"                
## [11] "longitude"                "x"                       
## [13] "y"

EDA

In our first step at exploratory data analysis we look at the first 5 rows of the Landfill. A few columns of note is the project name of the landfill, its location, the amount of waste, the project type and the start date of the project.

landfill_name project_name state waste_in_place_tons lfg_energy_project_type project_start_date
Anchorage Regional Landfill Project #1 AK 10400368 Reciprocating Engine 2012-07-31
Anchorage Regional Landfill Project #1, Expansion #1 AK 10400368 Reciprocating Engine 2014-01-01
Capitol Disposal Landfill NA AK 950804 Unknown NA
Central Peninsula Landfill (CPL) Project #1 AK 1721799 Cogeneration 2023-01-01
Kodiak Island Borough Landfill NA AK 228688 Unknown NA

We also look at the first 5 rows of the AgSTAR Anaerobic Digester data. A few columns of note is the project name of the digester, its location, the project type, the type of animal on the farm, the year of operation, and the end use of the bio gas.

project_name state project_type animal_farm_type_s year_operational biogas_end_use_s
Cargill - Sandy River Farm Digester AR Farm Scale Swine 2008 Flared Full-time
Butterfield RNG Digester AZ Farm Scale Dairy 2021 Pipeline Gas
Caballero Dairy Farms Digester AZ Farm Scale Dairy 2022 Pipeline Gas
Paloma Dairy Digester AZ Farm Scale Dairy NA CNG
Stotz Southern Dairy Digester AZ Farm Scale Dairy 2011 Electricity

Project Breakdown

We now look at RNG project breakdown by operational year. Here we see how projects have ramped up over the years. We also see the increase in AgStar projects year by year.

In looking at RNG the operational project count by state, we see California leading the charge, followed by Pennsylvania and New York.

Project Type Breakdown

From a map perspective, we see a heat map of the number of landfill projects by state. Again California leads the charge in terms of landfills, followed by Michigan. We also notice Wyoming does not have any landfills that are allocated for landfill biogas projects.

From a map perspective, we see a heat map of the number of digester projects by state. Again California leads the charge in terms of landfills, followed by Wisconsin and New York. We also notice the absence of digester projects for states such as Nevada, New Mexico, and surprisingly Louisiana.

RNG Facility Locations

Landfill RNG Project Forecast

The outlook for Landfill RNG projects looks bright. We see over 30 Billion tons of waste candidate projects, as well as over 40 Billion tons of waste landfills that are either in the planning state or in currently being constructed.

Ag RNG Production Forecast

The outlook for AgStar projects looks bright as well. We see over 100,000 animals being used to produce biogas.

Bio Gas Generation Estimate/day (cubic ft)

Electricity Generation Estimate/year (KWH)

Demand

Project Count by End Use

Power Generation

Carbon Zero

Ag Emissions Reductions

Landfill Gas Emissions Reductions

Landfill Gas Emissions Avoidance

Summary

In looking at the data I believe the outlook for RNG is positive. Through the direction of the EPA we see states such as California, Michigan and New York leading the charge. We even see conservative states like Texas investing in RNG projects.

With more municipalities buying compressed natural gas vehicles we will continue to see demand rise. We also see electricity utilities replacing their current fossil fuel based natural gas with renewable natural gas.

As we embark on the road to Carbon zero, we see the use of RNG has an effect as data shows an increase in emission reduction and avoidance.